{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0,
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction-to-computational-methods\" data-toc-modified-id=\"Introduction-to-computational-methods-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction to computational methods</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-file\" data-toc-modified-id=\"Data-file-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Data file</a></span></li><li><span><a href=\"#Algorithm\" data-toc-modified-id=\"Algorithm-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Algorithm</a></span></li><li><span><a href=\"#Python-code\" data-toc-modified-id=\"Python-code-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Python code</a></span></li><li><span><a href=\"#Python-modules/libraries\" data-toc-modified-id=\"Python-modules/libraries-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Python modules/libraries</a></span></li><li><span><a href=\"#convert-flow-to-a-numpy-array\" data-toc-modified-id=\"convert-flow-to-a-numpy-array-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>convert flow to a numpy array</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "# Introduction to computational methods\n",
    "\n",
    "This notebook introduces a python program to integrate daily average flow into and out of\n",
    "the tailings management facility (TMF), and the volume of water in the TMF through time.\n",
    "\n",
    "## Data file\n",
    "This notebook assumes that a comma-separated variable (csv) file named `data/july2016-tmf-flow.csv`\n",
    "exists in the data folder in the current working directory. The file contains three columns with headers:\n",
    "\n",
    "`date` `outflow` `inflow`\n",
    "\n",
    "The first 4 lines of the file are:\n",
    "\n",
    "`date,outflow,inflow\n",
    "2016-07-01,0.0703,0.1181\n",
    "2016-07-02,0.066,0.1121\n",
    "2016-07-03,0.0621,0.1079`\n",
    "\n",
    "The data in the csv file is in the following format\n",
    "\n",
    "`date` is in `yyyy-mm-dd`\n",
    "\n",
    "`outflow` is the daily average outflow in $m^3/s$\n",
    "\n",
    "`inflow` is the daily average inflow in $m^3/s$\n",
    "\n",
    "\n",
    "\n",
    "## Algorithm\n",
    "The volume of water in the TMF on day $t$ of the computation is $V(t)$, the daily average outflow is $Q_{out}$\n",
    "and the daily average inflow is $Q_{out}$\n",
    "\n",
    "The basic computational algorithm is:\n",
    "\n",
    "1. Read in the date, $Q_{out}$ and $Q_{out}$ data from the file `july2016-tmf-flow.csv`.\n",
    "2. Compute the change in volume over the day $\\Delta V = (Q_{in} - Q_{out})\\Delta t$\n",
    "3. Update the volume of the TMF: $V(t+\\Delta t) = V(t) + \\Delta V$\n",
    "4. Loop through all dates in the file until complete."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Python code\n",
    "We'll introduce python concepts as we go in the course, so we don't expect you to fully understand\n",
    "this code yet. We'll therefore only provide a cursory explanation here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Python modules/libraries\n",
    "It is good practice to import libaries at the beginning of a program. Libaries are collections of python code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `import` command tells python to import the library. The `as` specifies a user - chosen nickname for library\n",
    "to be used in this program. So for this program, the `numpy` library will also be known as `np`.\n",
    "\n",
    "We are importing four libraries or library sections:\n",
    "\n",
    "`numpy` is a library with numerical - related codes. We'll use it alot in the course.\n",
    "\n",
    "`pandas` is a libary with codes to manipulate tabular data, that is data organized in rows and columns.\n",
    "\n",
    "`matplotlib` is a vast library for plotting. We don't need the whole library, but only two subsections.\n",
    "\n",
    "`matplotlib.pyplot`, nicknamed `plt` is a subsection containing so-called handle graphics. We'll use it alot.\n",
    "\n",
    "`matplotlib.dates`,  which we nickname `mdates` is a specialty subsection containing code for manipulating date formats.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>outflow</th>\n",
       "      <th>inflow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>0.1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-02</td>\n",
       "      <td>0.0660</td>\n",
       "      <td>0.1121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-03</td>\n",
       "      <td>0.0621</td>\n",
       "      <td>0.1079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-04</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.1115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-05</td>\n",
       "      <td>0.0574</td>\n",
       "      <td>0.1108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  outflow  inflow\n",
       "0  2016-07-01   0.0703  0.1181\n",
       "1  2016-07-02   0.0660  0.1121\n",
       "2  2016-07-03   0.0621  0.1079\n",
       "3  2016-07-04   0.0599  0.1115\n",
       "4  2016-07-05   0.0574  0.1108"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use pandas to read in csv data\n",
    "data_file = \"data/july2016-tmf-flow.csv\"\n",
    "df_flow = pd.read_csv(data_file)\n",
    "df_flow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pd.read_csv` command from the `pandas` library reads all the rows and columns from the file `july2016-tmf-flow.csv`.\n",
    "\n",
    "We could have also written the command as `pandas.read.csv`, but we used our nickname `pd` to save some typing.\n",
    "\n",
    "Here we see the power of the pandas library. This command contains code to open the file, read in each line of data from the\n",
    "csv file,and then stores that data in a `dataframe` that we named `flow`. You can think of a `dataframe` as rows and\n",
    "columns of data. In our case, the data in the `dataframe` is dates, outflows and inflows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## convert flow to a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of days in file: 31\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([['2016-07-01', 0.0703, 0.1181],\n",
       "       ['2016-07-02', 0.066, 0.1121],\n",
       "       ['2016-07-03', 0.0621, 0.1079],\n",
       "       ['2016-07-04', 0.0599, 0.1115]], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow = df_flow.values\n",
    "\n",
    "# find number of days of data (number of rows in array)\n",
    "\n",
    "n = flow.shape[0]\n",
    "print(f\"Number of days in file: {n}\")\n",
    "flow[:4, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This line of code uses the `.shape` method, which returns the dimension (number of rows and columns) in the dataframe\n",
    "`flow`. `.shape[0]` is the number of rows, or in our case, the number of days of outflow and inflow data in the dataframe,\n",
    "which we assign to the variable `n`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allocate np arrays for time and volume for calculations -\n",
    "# doing this in advance makes python run faster\n",
    "\n",
    "v = np.zeros(n, float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `zeros` from the numpy library creates an array of zeros. Python runs faster if the arrays used for computation\n",
    "are defined in advance, versus on the fly. Here we create an array `v` of `n` zeros of type `float`. Each element in\n",
    "`v` will store the volume of water in the TMF on a specific day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns in csv file: Index(['date', 'outflow', 'inflow'], dtype='object')\n",
      "type of the timestap object: <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
     ]
    }
   ],
   "source": [
    "# print  the columns in the dataframe\n",
    "print(f\"Columns in csv file: {df_flow.columns}\")\n",
    "\n",
    "# convert column called date to pandas date format\n",
    "# and store in column called yearmonthday\n",
    "\n",
    "df_flow[\"yearmonthday\"] = pd.to_datetime(df_flow[\"date\"])\n",
    "\n",
    "# see the data in the column yearmonthday\n",
    "print(f\"type of the timestap object: {type(df_flow['yearmonthday'][0])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first print the names of the columns in the dataframe that were read in from the `csv` file.\n",
    "We do this using the `.columns` method on the `flow` dataframe; that is `flow.columns` returns the indices of\n",
    "the columns in the dataframe.\n",
    "\n",
    "The command `flow[\"yearmonthday\"] = pd.to_datetime(flow[\"date\"]` uses the pandas method `.to_datetime`\n",
    "to convert the data in the column indexed with `date` in the dataframe `flow` to a date format that pandas\n",
    "understands, and then stores those panda dates in the dataframe `flow` in a new column with index `yearmonthday`.\n",
    "\n",
    "We then print out the type of the data in the column `yearmonthday` to prove that it is now a date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# set volume in TMF at time zero to 8.1 x 10^6 m^3\n",
    "\n",
    "v[0] = 8.1e6\n",
    "\n",
    "\"\"\"\n",
    "loop through and compute volume through time\n",
    "since flow is in m^3/s, and flows are daily average flows, must convert\n",
    "to m^3 in a day by multiplying by 86400 s/d\n",
    "\"\"\"\n",
    "seconds_per_day = 86400.0\n",
    "inflow = flow[:, 1]\n",
    "outflow = flow[:, 2]\n",
    "for i in range(n - 1):\n",
    "    v[i + 1] = v[i] + (inflow[i] - outflow[i]) * seconds_per_day\n",
    "\n",
    "df_flow[\"volume\"] = v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the guts of the computation. We first set the volume in the TMF at time zero to $8.1\\times 10^6 m^3$.\n",
    "\n",
    "Then we loop through the days in the dataframe and compute the change in volume over each day as described above:\n",
    "$V(t+\\Delta t) = V(t) + (Q_{in} - Q_{out})\\Delta t$, where $\\Delta t$ is $86400$ seconds (one day).\n",
    "\n",
    "In python we use a `for` loop. The command `for i in range(n - 1):` sets `i` to `0`, then enters the loop and when finished the loop,\n",
    "sets `i` to `1`, enters the loop,..., `n-1`.\n",
    "On the last time through the loop, `i=n-1` so that when we compute`v[i+1]` for the last time, we are computing `v[n]`.\n",
    "\n",
    "The rest of the code is used to generate the plot of outflow and inflow over time in on subplot,\n",
    "and volume of water in the TMF over time in another subplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make some plots\n",
    "\n",
    "# format for the x axis label to be month - day eg 07-03 for July 3\n",
    "# format string for dates that will be plotted on the x axis\n",
    "\n",
    "myFmt = mdates.DateFormatter(\"%m-%d\")\n",
    "\n",
    "# in the plot ax, plot outflow and inflow as separate lines\n",
    "\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 10]\n",
    "plt.plot(df_flow[\"yearmonthday\"], df_flow[\"inflow\"], label=\"Inflow\")\n",
    "plt.plot(df_flow[\"yearmonthday\"], df_flow[\"outflow\"], label=\"Outflow\")\n",
    "plt.legend()\n",
    "ax = plt.gca()\n",
    "fig = plt.gcf()\n",
    "ax.set(ylabel=\"flow $m^3/s$\", xlabel=\"date\")\n",
    "ax.xaxis.set_major_formatter(myFmt)\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # in second plot, plot the volume in TMF in m^3, which is stored in the \"volume\" column\n",
    "plt.plot(df_flow[\"yearmonthday\"], df_flow[\"volume\"], label=\"Volume in TMF\")\n",
    "plt.legend()\n",
    "ax = plt.gca()\n",
    "fig = plt.gcf()\n",
    "ax.set(ylabel=\"flow $m^3/s$\", xlabel=\"date\")\n",
    "ax.xaxis.set_major_formatter(myFmt)\n",
    "fig.autofmt_xdate()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "all",
   "formats": "ipynb",
   "notebook_metadata_filter": "all",
   "text_representation": {
    "extension": ".py",
    "format_name": "percent",
    "format_version": "1.2",
    "jupytext_version": "0.8.6"
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "nbsphinx": {
   "execute": "never"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}